We obtain attributions using a technique called Integrated Gradients [1]} that allows attributing the prediction \(f(x)\)  to the inputs \(x\) . We use the minimum value of each input feature as its baseline input \(x^{\prime }\)  since at this minimum valued baseline we get a prediction close to zero probability of rain. We compute gradients of each prediction at points along the straightline path from the baseline to the actual input. More formally,
\(IntegratedGrads(x) ::= (x - x^{\prime }) \times \int _{\alpha =0}^1 \frac{\partial f(x_{\alpha })}{\partial x} d\alpha \) 
